TAR Course

The term “TAR” as we use it means electronic document review enhanced by active machine learning, a type of specialized Artificial Intelligence. Our method of AI-enhanced document review is called Hybrid Multimodal Predictive Coding 4.0. By the end of the course you will know exactly what this means. You may even grok the above graphic. Reading the below graphic that uses the new Sans Forgetica font should help you to remember.

By the end of the TAR Course you will understand the importance of using all varieties of legal search, for instance: keywords, similarity searches, concept searches and AI driven probable relevance document ranking. That is the Multimodal part. The Hybrid part refers to the partnership with technology, the reliance of the searcher on the advanced algorithmic tools. It is important than Man and Machine work together, but that Man remain in charge of determining relevance. The predictive coding algorithms and software are used to enhance the lawyers, paralegals and law tech’s abilities, not replace them

By course end you will also know what IST means, literally Intelligently Spaced Training, where you keep training until first pass relevance review is completed, a type of Continuous Active Learning, which Grossman and Cormack call CAL.

The course begins with this introductory video by Ralph Losey welcoming you to the TAR Course. Ralph makes multiple video appearances throughout the course. More videos will be added from time to time to keep the materials current. Students are invited to leave comments and questions at the bottom of each class.

With a lot of hard work you can complete this online training program in a long weekend, but most people take a few weeks. After that, this course can serve as a solid reference to consult during your complex document review projects. It is also recommended that you follow Losey’s personal blog, e-DiscoveryTeam, to stay current.

We call our latest version of AI enhanced document review taught here “Predictive Coding 4.0.” We call it version 4.0 because it substantially improves upon and replaces the methods and insights we announced in our October 2015 publication – Predictive Coding 3.0. In the First Class of the TAR Course we explain the history of predictive coding software and methods in legal review, including versions 1.0 and 2.0. Unfortunately, most vendors are still stuck in these earlier methods. If you have tried predictive coding and did not like it, then the probable reason is that you used the vendors recommended, but wrong method. Either that, or the software was to blame, but it is probably the method. Many lawyers report that they attain better results when they follow their own methods, not the vendors default methods.

Most vendors are still promoting use of random based control sets based on a misunderstanding of statistics and search. The use of control sets is simply wrong and a waste of time. We never saw any of these same vendors at TREC and for good reason. They do not keep up with the latest developments in search science. They are a business. We are not. The e-Discovery Team is a group of lawyers, lead by Ralph Losey, a practicing attorney. We are lawyers sharing what we know with other lawyers (and vendors).

We offer this information for free on this blog to encourage as many people as possible in this industry to get on the AI bandwagon. Predictive coding is based on active machine learning, which is a classic, powerful type of Artificial Intelligence (AI). Our Predictive Coding 4.0 method is designed to harness this power to help attorneys find key evidence in ESI quickly and effectively.

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PREREQUISITES

Familiarity with these two websites is a prerequisite for this course:

TECHNOLOGY ASSISTED REVIEW (TAR), which is also called Computer Assisted Review (CAR). General Introduction to the e-Discovery Team’s approach to document review using active machine learning, a type of specialized Artificial Intelligence.

LEGAL SEARCH SCIENCE. The Team’s introduction to this new interdisciplinary field. It is concerned with the search, review, and classification of large collections of electronic documents to find information for use as evidence in legal proceedings, for compliance to avoid litigation, or for general business intelligence.

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28 Responses to TAR Course

[…] minute video. It now serves as the core video introduction to the e-Discovery Team’s free TAR Course. It is found in the first of the sixteen classes in the Course. I also revised and improved the […]

The other gift is your humble apology for the 1.0 protocol. I do not think it necessary as you were first to black letter. Yet an apology for impact is so rare in law or technology that it may persuade those who were turned off by seed set production to try again. I will be recommending your course to our ACEDS community. I’ve heard that EDRM is putting pencil to paper on TAR.

Your “four door” photo suggests that CAL uses control sets. Maura and I coined and trademarked the term CAL so that we could control what is described as CAL. No method properly described as CAL uses control sets.

[…] on AI-enhanced document review, new material has been added to the e-Discovery Team’s TAR Course. The new content includes two video lectures that provide examples of applications of the methods […]

[…] of them all, finding the needles of relevance in cosmic-sized haystacks of irrelevant noise. TARcourse.com. We now know what is required to do e-discovery correctly. EDBP.com. We have the software and […]

[…] and pertain to e-discovery. They show the application of the principles in legal search. See eg TARcourse.com. The principles have obvious applications in all aspects of society, not just the Law and […]

[…] have also made available a free online instruction program on predictive coding called the TARcourse.com. The flow-chart shown below is fully explained in the course. We have done or supervised hundreds […]

[…] to testify at any time to explain my now open-source methodology, Predictive Coding 4.0. See Eg. TARcourse.com. Most good experts are. But, so far, there have been no challenges, nor any reason for disputes. […]

[…] open-sourced eight-step process for document review using predictive coding. (Classes 10-16 of the TARcourse.com) This new Key Players diagram is another way of describing the iterative process that makes up the […]

[…] Doors Are Thrown Open to all 85-Classes of the e-Discovery Team Training Program. I also created a TAR Course last year, again free, which I continue to update. Announcing the e-Discovery Team’s TAR Training […]

[…] search methods, such as active machine learning (predictive coding), at least among the elite. See TARcourse.com. There is still some disagreement on TAR methods, especially when you include the many pseudo […]

[…] estimate that 98% of lawyers today do not, then hire an expert. (Or take the time to learn, see eg TARcourse.com.) Your vendor probably has a couple of search experts. There may also be a lawyer in town with this […]

[…] whould instead of focused on methods. The e-Discovery Team methods are spelled out in detail in the TAR Course. Maybe that is what Entrata followed? Probably not. Maybe, God forbid, Entrata used random driven […]

[…] Ralph isn’t just interested in picking apart current cases, though; he wants every attorney to have the opportunity to master ediscovery. To that end, he offers a free 85-class training program in electronic discovery law as well as an advanced program specifically focused on his current specialty, technology-assisted review (TAR). […]

About the Blogger

Ralph Losey is a practicing attorney and shareholder in a national law firm with 50+ offices and over 800 lawyers where he is in charge of Electronic Discovery. All opinions expressed here are his own, and not those of his firm or clients. No legal advice is provided on this web and should not be construed as such.

Ralph has long been a leader of the world's tech lawyers. He has presented at hundreds of legal conferences and CLEs around the world. Ralph has written over two million words on e-discovery and tech-law subjects, including seven books. He is also the founder of Electronic Discovery Best Practices, and e-Discovery Team Training, an online education program that arose out of his five years as an adjunct professor teaching e-Discovery and Evidence at the UF School of Law. Ralph is also publisher and principle author of this blog and many other instructional websites.

Ralph is a specialist who has limited his legal practice to electronic discovery and tech law since 2006. He has a special interest in software and the search and review of electronic evidence using artificial intelligence, and also in general AI Ethics. issues. Ralph was the only private lawyer to participate in the 2015 and 2016 TREC Recall Track of the National Institute of Standards and Technology and prior to that competed successfully in the EDI Oracle research.

Ralph has been involved with computers, software, legal hacking and the law since 1980. Ralph has the highest peer AV rating as a lawyer and was selected as a Best Lawyer in America in four categories: Commercial Litigation; E-Discovery and Information Management Law; Information Technology Law; and, Employment Law - Management. Ralph also received the "Most Trusted Legal Advisor" industry award for 2016-17 by the Masters Conference. His full biography may be found at RalphLosey.com.

Ralph is the proud father of two children, Eva Losey Grossman, and Adam Losey, a lawyer with cyber expertise (married to another cyber expert lawyer, Catherine Losey), and best of all, husband since 1973 to Molly Friedman Losey, a mental health counselor in Winter Park.

Sedona Principles 3rd Ed

1. Electronically stored information is generally subject to the same preservation and discovery requirements as other relevant information.

2. When balancing the cost, burden, and need for electronically stored information, courts and parties should apply the proportionality standard embodied in Fed. R. Civ. P. 26(b)(2)(C) and its state equivalents, which require consideration of importance of the issues at stake in the action, the amount in controversy, the parties’ relative access to relevant information, the parties’ resources, the importance of the discovery in resolving the issues, and whether the burden or expense of the proposed discovery outweighs its likely benefit.

3. As soon as practicable, parties should confer and seek to reach agreement regarding the preservation and production of electronically stored information.

4. Discovery requests for electronically stored information should be as specific as possible; responses and objections to discovery should disclose the scope and limits of the production.

5. The obligation to preserve electronically stored information requires reasonable and good faith efforts to retain information that is expected to be relevant to claims or defenses in reasonably anticipated or pending litigation. However, it is unreasonable to expect parties to take every conceivable step or disproportionate steps to preserve each instance of relevant electronically stored information.

6. Responding parties are best situated to evaluate the procedures, methodologies, and technologies appropriate for preserving and producing their own electronically stored information.

7. The requesting party has the burden on a motion to compel to show that the responding party’s steps to preserve and produce relevant electronically stored information were inadequate.

8. The primary source of electronically stored information to be preserved and produced should be those readily accessible in the ordinary course. Only when electronically stored information is not available through such primary sources should parties move down a continuum of less accessible sources until the information requested to be preserved or produced is no longer proportional.

9. Absent a showing of special need and relevance, a responding party should not be required to preserve, review, or produce deleted, shadowed, fragmented, or residual electronically stored information.

10. Parties should take reasonable steps to safeguard electronically stored information, the disclosure or dissemination of which is subject to privileges, work product protections, privacy obligations, or other legally enforceable restrictions.

11. A responding party may satisfy its good faith obligation to preserve and produce relevant electronically stored information by using technology and processes, such as data sampling, searching, or the use of selection criteria.

12. The production of electronically stored information should be made in the form or forms in which it is ordinarily maintained or in a that is reasonably usable given the nature of the electronically stored information and the proportional needs of the case.

13. The costs of preserving and producing relevant and proportionate electronically stored information ordinarily should be borne by the responding party.

14. The breach of a duty to preserve electronically stored information may be addressed by remedial measures, sanctions, or both: remedial measures are appropriate to cure prejudice; sanctions are appropriate only if a party acted with intent to deprive another party of the use of relevant electronically stored information.